Objective To explore the value of artificial intelligence(AI)based multi-parameter coronary CT angiography(CCTA)features combined with clinical indicators for predicting coronary plaque progression.Methods Totally 143 coronary atherosclerosis(AS)patients were retrospectively enrolled and divided into progression group(arithmetic average annual growth rate of plaque load＞1％,n=73)and non-progression group(arithmetic average annual growth rate of plaque load＜1％,n=70).The baseline clinical data,CT-derived fractional flow reserve(CT-FFR),perivascular fat attenuation index(FAI),and quantitative plaque features were collected and compared between groups.For variables being statistically different between groups,those had collinearity with others were excluded,and then multivariable logistic regression was used to screen independent predictors of plaque progression from the retained variables,and a combined model was constructed.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Progression group had higher proportions of hypertension and diabetes,higher apolipoprotein A1(ApoA1)and high-sensitivity C-reactive protein(hs-CRP)levels but lower high-density lipoprotein cholesterol(HDL-C)levels than non-progression group(all P＜0.05).Progression group showed smaller minimum lumen area and lower CT-FFR,but greater degree of lumen stenosis,total plaque volume,plaque load,non-calcified plaque volume,lipid-rich plaque volume,fibrolipid plaque volume and FAI values than non-progression group(all P＜0.05).Plaque types were different between groups(P＜0.05).Diabetes,low HDL-C,small minimum lumen area and large lipid-rich plaque volume were all independent predictors of plaque progression in patients with coronary AS(all P＜0.05),and the AUC of the combined model for predicting plaque progression was 0.859.Conclusion Multi-parameter CCTA features based on AI combined with clinical indicators could be used to effectively predict progression of coronary AS plaque.

Objective To explore the value of artificial intelligence(AI)based multi-parameter coronary CT angiography(CCTA)features combined with clinical indicators for predicting coronary plaque progression.Methods Totally 143 coronary atherosclerosis(AS)patients were retrospectively enrolled and divided into progression group(arithmetic average annual growth rate of plaque load＞1％,n=73)and non-progression group(arithmetic average annual growth rate of plaque load＜1％,n=70).The baseline clinical data,CT-derived fractional flow reserve(CT-FFR),perivascular fat attenuation index(FAI),and quantitative plaque features were collected and compared between groups.For variables being statistically different between groups,those had collinearity with others were excluded,and then multivariable logistic regression was used to screen independent predictors of plaque progression from the retained variables,and a combined model was constructed.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Progression group had higher proportions of hypertension and diabetes,higher apolipoprotein A1(ApoA1)and high-sensitivity C-reactive protein(hs-CRP)levels but lower high-density lipoprotein cholesterol(HDL-C)levels than non-progression group(all P＜0.05).Progression group showed smaller minimum lumen area and lower CT-FFR,but greater degree of lumen stenosis,total plaque volume,plaque load,non-calcified plaque volume,lipid-rich plaque volume,fibrolipid plaque volume and FAI values than non-progression group(all P＜0.05).Plaque types were different between groups(P＜0.05).Diabetes,low HDL-C,small minimum lumen area and large lipid-rich plaque volume were all independent predictors of plaque progression in patients with coronary AS(all P＜0.05),and the AUC of the combined model for predicting plaque progression was 0.859.Conclusion Multi-parameter CCTA features based on AI combined with clinical indicators could be used to effectively predict progression of coronary AS plaque.

Objective:To use whole brain vessel wall imaging combined with whole brain perfusion to explore the relevant high-risk features of imaging that caused the occurrence of ischemic stroke events.Method:A retrospective analysis was conducted on 60 patients with suspected atherosclerosis of middle cerebral artery(MCA)who admitted to The Second Affiliated Hospital of Bengbu Medical University from Oct.2021 to Mar.2023.All patients underwent the examination of high-resolution magnetic resonance vessel wall imaging(HRMR-VWI).According to the high signal values of diffusion weighted imaging(DWI),or the specifically clinical symptoms that were relevant with MCA blood-supplied area in clinical practice,they were divided into symptom group(36 cases)and non-symptom group(24 cases).The differences of the imaging characteristics of plaque,the status of collateral circulation and hemodynamic changes between the two groups were compared.The receiver operating characteristic(ROC)curve was drawn to appear the diagnostic efficiencies of the single factor model and the combined diagnostic model.Result:Compared with the non-symptom group,the patients of the symptom group had longer plaques,larger remodeling index,higher degree of plaque enhancement,more plaques located on the upper or posterior wall,more eccentric plaques,poorer status of collateral circulation,larger relative mean transit time(rMTT),larger relative time to peak(rTTP),and larger relative time to peak of residual function(rTmax).ROC curve analysis showed that the area under curve(AUC)values of the above four indicators were all lower than that of the combined diagnostic models of them(0.911).Conclusion:HRMR-VWI combined with compute tomography perfusion(CTP)can clarify the value of that in predicting ischemic events,and optimize the assessment system based on risk factors such as MCA atherosclerotic plaque,collateral status of leptomeningeal and cerebral perfusion status.